What's That Race Worth

Anyone who runs a lot of races usually encounters many distances, and it’s normal to wonder how your times at the different distances relate to one another. Was my recent half marathon better than that fast 10K I ran last year? Given my recent races, what sort of 5K time can I shoot for next month? The table included with this article should make it easy for you to answer these kinds of questions.

When I began racing in 1994, it was natural for me, a retired scientist, to want to equate my times for the different distances I ran. Many people had come up with many ways of making these comparisons, but these had problems: the results obtained from the various methods disagreed greatly; they didn’t include many common racing distances; and graphing their results (plotted versus distance) would often yield jagged curves instead of smooth ones. Two of the better existing approaches are those of James B. Gardner and J. Gerry Purdy (Computerized Running Training Programs, TAFNEWS Press, 1992) and Jack Daniels and Jimmy Gilbert (Oxygen Power). However, I envisioned a better table, one that would:

-include time increments small enough to allow runners to approximated their time at any distance.

Finding a Database

My major obstacle in constructing a useful table was in obtaining experimental data. I needed race results of many runners - from all age groups - who have raced at different distances. I tried to use my running club, the Oregon Track Club Masters, as a source of data, but it had too few members, and its database hadn’t been well maintained.

Who might have a large database? With nothing to lose, I contacted the New York Road Runners Club (NYRRC) and spoke with Tom Kelley, who told me that the NYRRC sponsors many races each year and all of the results - including the age of every finisher - are stored on a computer. Tom referred me to Ben Grundstein to help me access some of this vast database. Ben is responsible for maintaining all racing information generated by the NYRRC. His records are so meticulous that for a given year, he can output the date, site and time achieved by a given runner in every NYRRC-sponsored race in which that runner participated. He has also developed a system for assigning point values, allowing him to predict from a runner’s age and results of a few races what that runner would do at another distance.

Ben and I disagreed about some issues. It was his view that if two runners, one age 16 and the other 40, had identical 10K times, the 16-year-old would have a much better mile time than the older runner, and hence, the table I sought couldn’t cover both. That’s most likely correct, but I suggested that we try to come up with the best approximation possible. He added that his computers had the capability, via his system of point values, to predict a runner’s time at any distance, based on the runner’s age and the results of a few races. Although I believed him, I wanted to come up with something simple that could give useful results to the large population of runners, rather than nearly exact results to only a chosen few.

Ben agreed to send me some of his data, and a week later, I received about 80 pages of fascinating information, covering runners from less than 10 years old to more than 70, broken down into 5 and 10-year increments and with separate tabulations for men and women. Every race run by each of these runners during the previous 12 months was included.

A Look at the Times

A cursory comparison of the men’s and women’s data showed some significant differences. The table shown here applies only to men. (If it’s well received, I’ll produce a similar one for women.)

Based on Ben’s teenage speed argument, I omitted the data on runners younger than 20. In addition, because my analysis arbitrarily correlates a runner’s time at a given distance with his 10K time, I omitted the data on runners who hadn’t raced a 10K, no matter how many other races they had run. This included about 10 percent of the runners covered.

I then converted the remaining data to coefficient form by defining a factor, R, that relates a runner’s pace (meters per second) at a given distance with his 10K pace. I won’t bore or confuse anyone with the details of my analysis.

This lengthy procedure has resulted in a table that best fits the actual data on many runners, ages 20 and over, covering hundreds of races. To get your estimate for any distance, find your 10K time and read straight across. All 10K times are listed in 1-minute increments, except the one in the first row, which I sisted so that I could include the 4:00 mile. Note that the NYRRC data that produced this table didn’t include any near-world-record marks, so the table doesn’t extend into world-record range. Even so, the table turns out to be completely consistent with world record results and, more important, useful to a great number of runners.

Donn Kirk is a retired NASA research scientist, currently in the Physics Department at the University of Oregon. Ben Grundstein is an electrical and computer consultant whose 20-year running career encompasses running and race directing of road races from 800m through 100 miles. At age 51, he can still run a sub-5:00 mile.